What is a DQ rule?
Data Quality Rules facilitates you to create a DQ (Data Quality) definition and define nine specific validation checks based on Range, Data Length, Column Reference/Specific Value, List of Value/Code, Null Value, Blank Value, Referential Integrity, Duplicity, and Custom Check/Business.
What is DQ in ETL?
For each stage table, ETL picks up the DQ Rules (Query) defined in the DQ meta data table and executes to identify which all are the records violating the rules. Once the DQ process is completed, there is another ETL/Process that picks up all the bad quality records from Stage table and moves to Reject tables.
What are some examples of validation rules?
Some other common examples of data validation rules that help maintain integrity and clarity include: Data type (ex. integer, float, string) Range (ex. A number between 35-40) Uniqueness (ex. Postal code) Consistent expressions (ex. Using one of St., Str, Street) No null values
How to apply data validation criteria?
How to Apply Data Validation Criteria. Apply Data Validation to a Cell or Range. To apply data validation to a cell or range: 1. Select the desired cell or range and then select DATA→Data Tools→Data Validation . 2.
What is the formula for data validation?
To use data validation to allow a list of specific characters only, you can use a rather complicated array formula based on the COUNT, MATCH, and LEN functions. In the example shown, data validation is applied with this formula: =COUNT(MATCH(MID(B5,ROW(INDIRECT(“1:”&LEN(B5))),1),allowed&””,0))=LEN(B5)
How do I use data validation?
When users click in a cell that has data entry requirements, you can display a message that explains what data is valid. Select the cells where you want to prompt users for valid data entries. On the Data tab, click Data Validation > Data Validation. On the Input Message tab, select the Show input message when cell is selected check box.